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This paper presents a fault-tolerant control (FTC) algorithm for four-wheel independently driven and steered (4WID/4WIS) electric vehicle. The Extended Kalman Filter (EKF) algorithm is utilized in the fault detection (FD) module so as to estimate the in-wheel motor parameters, which could detect parameter variations caused by in-wheel motor fault. A motion controller based on sliding mode control (SMC) is able to compute the generalized forces/moments to follow the desired vehicle motion. By considering the tire adhesive limits, a reconfigurable control allocator optimally distributes the generalized forces/moments among healthy actuators so as to minimize the tire workloads once the actuator fault is detected. An actuator controller calculates the driving torques of the in-wheel motors and steering angles of the wheels in order to finally achieve the distributed tire forces. If one or more in-wheel motors lose efficacy, the FD module diagnoses the actuator failures first.

The distributed driving electric vehicle, uses four in-wheel motors as distributed power sources is a typical over-actuated system. Thus, this kind of vehicle has better stability potential and fault tolerance than the conventional one. In this paper, the general structure of fault-tolerance control (FTC) system based on control allocation is analyzed. And a reconfigurable driving force allocation strategy is proposed to ensure the trajectory tracking and stability when some motors’ faults occur. Both the constraints of tire force and actuators are taken into consideration. With motors’ faults treated as the constraints of actuators, FTC is integrated. For validation, the proposed allocation strategy is simulated in co-simulation environment based on Carsim and Matlab/Simulink.

The driving range of the electric vehicle (EV) greatly restricts the development of EVs. The vehicles waste plenty of energy on account of automobiles frequently braking under the city cycle. The regenerative braking system can convert the braking kinetic energy into the electrical energy and then returns to the battery, so the energy regeneration could prolong theregenerative braking system. According to the characteristics of robustness in regenerative braking, both regenerative braking and friction braking based on fuzzy logic are assigned after the front-rear axle’s braking force is distributed to meet the requirement of braking security and high-efficient braking energy regeneration. Among the model, the vehicle model and the mechanical braking system is built by the CRUISE software. The paper applies the MATLAB/SIMULINK to establish a regenerative braking model, and then selects the UEDC city cycle for model co-simulation analysis.

A new electric power steering system (EPS) dynamic friction model based on normalized Bouc-Wen model is given, as well as its structure form and model features. In addition, experimental method is used to identify corresponding parameters. In order to improve road feel feedback, this paper analyzes the shortcoming of traditional constant friction compensation control method and proposes a variable friction compensation control method which the friction compensation current changes according to the assist characteristic gain. Through simulation and real vehicle test verification, variable friction compensation control method eliminates the effect of basic assist characteristic, and improves the driver’s road feel under high speed.

Omni-directional electric vehicle built by our research group is an advanced electric vehicle whose four wheels can drive, steer and brake independently. The vehicle chassis system is composed of four in-wheel motors, four independent steer motors and electromagnetic brake system, and its control system is divided into logical control layer and underlying execution layer. The information exchange between these two layers is implemented by CAN bus. In this paper, the traction control logic for Omni-directional electric vehicle is developed. The study mainly involves two aspects: the vehicle states estimation and the traction control logic design. The vehicle states, including vehicle longitudinal velocity, lateral speed, side slip angle and yaw rate, etc, are estimated based on Extended Kalman Estimation and multiple degrees of freedom vehicle model.

Impact torque will be generated on the steering wheel when one tire suddenly blows out on high way, which may cause driver's psychological stress and result in driver's certain misoperations on the car. In this paper, the model of tire blowout vehicle was established; the tire blowout was detected based on the change rate of tire pressure, meanwhile, the rack force caused by tire blowout was estimated through a reduce observer; finally the compensation current was figured out to reduce the impact torque on the steering wheel. Results of simulation tests showed that the control strategy proposed in this paper can effectively reduce the impact torque on the steering wheel and reduce the driver's discomfort caused by tire blowout.

A full drive-by-wire electric vehicle, named Urban Future Electric Vehicle (UFEV) is developed, where the four wheels' traction and braking torques, four wheels' steering angles, and four active suspensions (in the future) are controlled independently. It is an ideal platform to realize the optimal vehicle dynamics, the marginal-stability and the energy-efficient control, it is also a platform for studying the advanced chassis control methods and their applications. A centralized control system of hierarchical structure for UFEV is proposed, which consist of Sensor Layer, Identification and Estimation Layer, Objective Control Layer, Forces and Motion Distribution Layer, Executive Layer. In the Identification and Estimation Layer, identification model is established by utilizing neural network algorithms to identify the driver characteristics. Vehicle state estimation and road identification of UFEV based on EKF and Fuzzy Logic Control methods is also conducted in this layer.

Electronically controlled air suspension (ECAS) systems have been widely used in commercial vehicles to improve the ride comfort and handling stability of vehicles, as it can adjust vehicle height according to the driving conditions and the driver's intent. In this paper, the vehicle height adjustment process of ECAS system is studied. A mathematical model of vehicle height adjustment is derived by combining vehicle dynamics theory and thermodynamics theory of variable mass system. Reasons lead to the problems of “over-charging”, “over-discharging” and oscillation during the process of height adjustment are analyzed. In order to solve these problems, a single neuron proportional-integral-derivative (PID) controller is proposed to realize the accurate control of vehicle height. By simulation and semi-physical rig test, the effectiveness and performance of the proposed control algorithm are verified.

This paper presents an integrated chassis controller with multiple hierarchical layers for 4WID/4WIS electric vehicle. The proposed systematic design consists of the following four parts: 1) a reference model is in the driver control layer, which maps the relationship between the driver's inputs and the desired vehicle motion. 2) a sliding mode controller is in the vehicle motion control layer, whose objective is to keep the vehicle following the desired motion commands generated in the driver control layer. 3) By considering the tire adhesive limits, a tire force allocator is in the control allocation layer, which optimally distributes the generalized forces/moments to the four wheels so as to minimize the tire workloads during normal driving. 4) an actuator controller is in the executive layer, which calculates the driving torques of the in-wheel motors and steering angles of the four wheels in order to finally achieve the distributed tire forces.

The passive fault-tolerant approach for four-wheel independently driven and steered (4WID/4WIS) electric vehicles has been investigated in this study. An adaptive control based passive fault-tolerant controller is designed to improve vehicle safety, performance and maneuverability when an actuator fault happens. The proposed fault tolerant control method consists of the following three parts: 1) a fault detection and diagnosis (FDD) module that monitors vehicle driving condition, detects and diagnoses actuator failures with the inequality constraints; 2) a motion controller that computes the generalized forces/moments to track the desired vehicle motion using Model Predictive Control (MPC); 3) a reconfigurable control allocator that redistributes the generalized forces/moments to four wheels with equality constrained optimization.

Four-wheel independent drive EV is driven by four brushless DC motors which are embedded in the wheel hubs. It enables each wheel's driving torque to be controlled independently. Due to the motors' torque and rotational speed easily measured, as well as the features of fast response and precise control, the EV enjoys obvious advantages over traditional vehicles in acceleration slip regulation. In this paper a novel acceleration slip regulation strategy for four-wheel independent drive EV is studied. The strategy includes a road identification module for the peak value of road adhesion coefficient and a slip regulation logic based on PID algorithm. Through comparing the current wheel slip ratio and the utilized adhesion coefficient with the typical roads' value, the identification module adopts the fuzzy control algorithm to recognize the similarity between the current road and the typical roads. Utilizing the similarity we can calculate the optimal slip ratio of the current road.

Four-wheel independent control electric vehicle is a new type of x-by-wire EV with four wheels independent steering and four wheels independent drive/brake systems. In order to take full advantage of the vehicle's performance potential, this paper presents a novel integrated chassis control strategy. In the paper, the strategy is designed by the hierarchical control structure and divided into integrated control layer and allocation layer. By this method, the control logical can be modularized and simplified. In the integrated control layer, Model Prediction Control (MPC) is adopted to design the integrated control unit, which belongs to be a kind of local optimization algorithm with feedback correction features. Using this method could avoid the system performance degradation caused by the control model mismatch. The control allocation layer is to optimally distribute the vehicle control forces to the steering/driving/brake actuators on each wheel.

As a new form of electric vehicle, Four-wheel-independent electric vehicle with X-By-Wire (XBW) inherits all the advantages of in-wheel motor drive electric vehicles. The vehicle steering system is liberated from traditional mechanical steering mechanism and forms an advanced vehicle with all- wheel independent driving, braking and steering. Compared with conventional vehicles, it has more controllable degrees of freedom. The design of the integrated vehicle dynamics control systems helps to achieve the steering, driving and braking coordinated control and improves the vehicle's handling stability. In order to solve the problem of lacking of vehicle state information in the integrated control, some methods are used to estimate the vehicle state of four-wheel-independent electric vehicles with XBW. In order to improve the estimation accuracy, unscented Kalman filter (UKF) is used to estimate the vehicle state variables in this paper.